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Ride Sharing Attitudes Before and During the COVID-19 Pandemic in the United States

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Abstract and Figures

We explore how the COVID-19 pandemic has changed individuals’ attitudes and perceptions toward sharing rides. In 2019, the year before the pandemic, we had implemented an online survey to understand US travelers’ attitudes and perceptions of modes, including transit and pooled ride-hailing. During the pandemic, in May 2020, we redistributed a portion of that survey to the same respondents. We compare the distributions of responses from before and during the pandemic and test the significance of these shifts. We found that while people’s dislike of sharing rides with strangers has not changed compared with before the pandemic, their willingness to share to save money has significantly changed. Also, more people are not OK with crowded buses and try to use modes that allow them to avoid others.
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TRANSPORT FINDINGS
Ride Sharing Attitudes Before and During the COVID-19 Pandemic in
the United States
Parastoo Jabbari
1 , Don MacKenzie
2
1 Department of Civil and Environmental Engineering, University of Washington (WA), 2 Department of Civil and Environmental Engineering, University of
Washington
Keywords: covid-19, travel behavior, transit ridership, ride-sharing
https://doi.org/10.32866/001c.17991
Findings
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)%#"#)())(#2#$)(()(N$*#)),!
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))!!$,)")$+$$)'(N
Research Question and Hypothesis
WBJ%#"#'(*!)#().W)W$"$''(#!$(*'($
#$#((#)!*(#((('()!!.#)'+!$(#$%)$#(+!!N
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$+'#"#)( # "%!$.'( + '+# "#. $ )( ()(L #+*!(Z
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('# $ $#2# (%(L ". + # ( ,!!N # )( %%' , )()
,)')%#"##+*!(Z)))*()$,'('#'(,)
$)'(N
Methods and Data
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'!))$%$%!Z()))*(#%'%)$#()$,'"$())'&*'('#
$+!(,)()'#'(N)(),)')',((#2#)##
'%$')+$'(#)))*()')$#()$)%#"N
#CABJL,*("/$##!*' S*' T)$''*)%')%#)(N
*'  (*'+. ) ' ( *#&*$' ' ,,(*( )$!")
(*'+.%')%)$#)$ #+*!(,$) #$*'(*'+.#CABJN '$"
FJB%')%#)(,$,'!!($#%')%)$##)CABJ(*'+.L
CHH #+*!( '(%$# )$ ) CACA (*'+.N  (*'+. #!*
($$"$'% &*()$#( # %(.$")' "(*'( '!) )$ ('#
Jabbari, Parastoo, and Don MacKenzie. 2020. “Ride Sharing Attitudes Before and During
the COVID-19 Pandemic in the United States.”
Findings
, November.
https://doi.org/10.32866/001c.17991.
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(!W'%$') "%!$."#) ())*( # ) CACA (*'+.N HH] $ %')%#)(
#)))).+*!!W)"$N
Findings
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' ())()!!.(#2#)N  !$-$# (#W'#  )() )()( ,)' )
0'#(# ) "# '# ( ),#%'( $ $('+)$#( ' ())()!!.
(#2#)S'( W!!'CABDTN
 2'() ())"#) )')( $, %')%#)( ! $*) ('#  ' ,)
()'#'(N#'!!#(X*#$"$')!Y$)$'#*'#)
%#"N  $('+ "#$' ()( '$" X(!)!. *#$"$')!Y )$
X*#$"$')!YN $,+'L ) #( # '(%$#(( ' #$) ())()!!.
(#2#)($#)!$-$#(#W'# )()Sp\ANETN(%$##)(,'
!'.*#$"$')!('#'(,)()'#'(L#))(#$)#
*($)%#"N
($#())"#),(X,$*!! )$(''( ,) $)'()(+(
"$#.Y # %')%#)( ,' (  )$ #) )' !+!$ '"#) $'
('"#)N(($,##*'BL)',(()'$"'#,))
'#)))*($'#*'#)WBJ#"#)#)))(
Findings 2
*'BK(%$#(()$(*'+.())"#)($'#*'#)%#"N%%'!)K'#',)()'#'((
Q+'.*#$"$')!M+'.%!(#)RN%%'')K,$*!! )$(''(,)$)'()(+("$#.Q()'$#!.
('M()'$#!.'RN$,'!)K)'.)$*()'#(%$'))$#$%)$#())!!$,")$+$$#)),)$)'%$%!
Q()'$#!.('M()'$#!.'RN$,'')K$#Z)"#)'+!!#$#'$,*(Q()'$#!.('M()'$#!.
'RN
())"#) )$,' ('#L *'# ) %#"N  !$-$# (#W
'#  )() #)( )) ) 0'# # ) ),$ ("%!( ( ())()!!.
(#2#)Sp\DNE-BAWDTN((*()())))'$0),#('#'(
#"$#.(#*'#)%#"##+*!('!((,!!#)$
(''()$(+"$#.$"%',)$')%#"N
 $('+ (/!() # '%$')'(%$#(( $' ) ())"#)X )'.)$
*()'#(%$'))$#$%)$#())!!$,")$+$$#)),)$)'%$%!NY
*'#)%#"%$%!'%$')()'$#'!+!($ '"#) ,))(
())"#) # ( $# ) !$-$# (#W'#  )() )( #( '
())()!!.(#2#)Sp\BNAC-BAWJTN(($#(()#),),)(-%)
*'#)%#"("'#(,'+()$ %)!()G)SBNI"T
%'))$'*(()'#("(($#N
!()())"#)(%2!!.)')#+*!(Z)))*()$,''$,
*((N+#)$*,$('+))'(%$#((#$))"%'$('"$'
$##)') # ('"#) )$'(L *'# ) %#" )
('"#) ( ()'$#'N  !$-$# (#W'#  )() Sp \ CND -BAWET
$#2'"( )) *'# ) %#" (#2#)!. "$' #+*!(  
%'$!",)'$,*(($"%',)$')%#"N
'#)))*($'#*'#)WBJ#"#)#)))(
Findings 3
*'CK%%#$#+*!'(%$#(($')%#")$("#+*!Z('(%$#(*'#)%#"$')
())"#)X$#Z)"#)'+!#$#'$,*(YN
($#$*')L,#())%$%!,'!'.*#$"$')!('#
'(,)$)'($')%#"L#))(#$)#"*N)
( # ( )) '(%$##)( ' #$, !(( ,!!# )$ %*) )) ($"$')
( )$ (+ "$#.N ( ( "%!)$#( $' '('# $"%#( #
)'#() #( ( ). ' !'. ()'*!# )$ % %$$! ' ('+(
$#$"!!.(*()#!N
WBJ((%$((')'#')$$!'#+*!(#)$(
,)%'W-()# "! $#)$#(N '$  $,#$*'("%! . L)$
-%!$',)',$('+0'#))))*("$#$!'#+*!(N*'
C ($,( )) *'# ) %#" EG] $ #+*!( $+' FA .'( $ 
#)))).()'$#!.(',))())"#)))X$#Z)"#
)'+!# $#  '$, *(YN  2*' !($ ($,( )) $!' #+*!(
()"$'()'$#!.)$,'('"#))#.$*#''(%$##)(N
))),$#$)$('+"$''()#(#'(%$#(((*)!()#
%'))$))))$!'#+*!((! '$,*((+#$')
%#"N
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Findings 4
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Findings 5
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Data
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Metadata
/6.,/"%)3302:.%*.(201&22/1("13*$,&1*%&2)"1*.("33*34%&2#&'/1&".%%41*.(3)&
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Findings 6
... Reluctance to share rides with strangers. Jabbari and MacKenzie (2020) United States Reluctance to share rides with strangers, increased by the COVID-19 pandemic. ...
... Willingness to share AV rides was found to be positively correlated with familiarity of the AV and ride-sharing technology possibly attenuating the risk-perception for this future transport mode. The aversion of sharing rides was reported to remain at a rather high level during the COVID-19 pandemic in the US according to Jabbari and MacKenzie (2020). Respondents in this questionnaire survey reported feeling uncomfortable with sharing a ride with strangers both before and during the pandemic, while in the latter case people appeared more reluctant to share rides to save money or even trying more to use transportation modes that avoid contact with other people compared to their responses before the pandemic. ...
Article
Full-text available
Shared electric automated vehicles (AVs) are advertised as the silver bullet for the sustainable transition of private internal combustion engine-based automobility by private and public entities. We explore the extent to which private automobility will be reconfigured into a private electric automated mobility regime or substituted by a shared electric automated mobility regime that could effectively address societal sustainability challenges. We draw from the multi-level perspective of technological transition, develop a conceptual model outlining possible transition advancements towards private and shared electric automated mobility and review pertinent literature supporting such developments. Our analysis reveals that shared, particularly pooled, mobility emerges slowly (niche level). Key actors resist a shift from private to shared electric automated mobility for economic (vehicle manufacturers), instrumental, affective, symbolic (users and societal groups), tax-revenue, governance and administrative (public authorities) reasons (regime level). The private automobility regime receives only moderate pressure from the socio-technical landscape pertaining to safety, congestion and environmental issues and effectively reacts by electrifying and automating vehicles (landscape level). We conclude that the most likely transition will primarily entail privately-owned electric AVs as opposed to shared (especially pooled) AVs, unless a landscape “shock” such as a climate breakdown, energy crisis or a significant political shift towards collective mobility exerts substantial pressure on the regime. Hence, the socioeconomic benefits of the so-called “three revolutions of automobility” could be diminished.
... At the individual level, it was found that most users used ridehailing less frequently during pandemic, although some used it more frequently than before (Loa et al., 2021). Further studies revealed that various factors affected users' willingness and behaviors in ride-hailing usage (Jabbari and MacKenzie, 2020;Mai et al., 2021;Nguyen-Phuoc et al., 2022;Nguyen-Phuoc et al., 2023;Sakib et al., 2023). A direct factor affecting the willingness for ride-hailing trip is the severity of regional COVID-19 infections (Chang and Miranda-Moreno, 2022;Tan et al., 2022;Wang et al., 2022c). ...
Article
Full-text available
The development of emerging mobility services, such as ride-hailing, has greatly changed urban mobility modes. However, the whole ride-hailing industry experienced significant disruptions during the COVID-19 pandemic, and there are still few studies investigating the associated lockdown's impacts on income levels and the derived inequality issues concerning disadvantaged stakeholders within the ride-hailing industry. Viewing this gap, this study utilizes a comprehensive dataset from a ride-hailing company in China and investigates the city-wide fluctuations in drivers' income across demographic subgroups during the pandemic. The results show that drivers' income quickly recovered to pre-pandemic levels after the lockdown ended. The income inequality (measured by the Gini Index) among drivers remained stable before and after the lockdown. Counterintuitively, groups that typically face systemic challenges in the workplace (i.e., female and older drivers) experienced less income loss during the lockdown and earned more than male and young drivers in the post-lockdown period. Working efficiencies across demographic subgroups were also found to be similar, and the underlying reason for higher income among female and older drivers is their commensurate working time. These demonstrate the limited long-term impacts of lockdowns on the ride-hailing industry and highlight its inclusivity by showing equal working efficiency and income for all demographic groups. The amount of working time, rather than other demographic factors like gender or age, is the major determinant of income level in the ride-hailing industry. Findings herein empirically investigate the influences of government lockdown regulations on the drivers' benefits and carry significant policy implications: involving potentially marginalized communities in ride-hailing employment could be considered to promote overall social equality and welfare, especially in the post-COVID recovery era.
... We call this formulation FreeMatch. We consider this formulation timely and significant, particularly in light of research indicating that a higher number of individuals have reported issues with crowded vehicles since the pandemic, compared to the pre-pandemic period [18]. With FreeMatch, our objective is to establish a framework that optimally utilises smaller vehicles with a maximum capacity of two, enhancing the appeal of ridesharing, in the forever changed post pandemic world. ...
Preprint
Full-text available
Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from conventional heuristic-based methods, commonly used to achieve timely solutions in large-scale ride-matching. We propose a novel approach that provides snapshot optimal solutions for various forms of one-to-one matching, ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant where the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.
... We call this formulation FreeMatch. We consider this formulation timely and significant, particularly in light of research indicating that a higher number of individuals have reported issues with crowded vehicles since the COVID-19 pandemic compared to the prepandemic period [28][29][30]. With FreeMatch, our objective is to establish a framework that optimally utilises standard commonplace vehicles with a maximum capacity capped at two, enhancing the appeal of ridesharing in accordance with travelers' altered preferences following the COVID-19 pandemic. ...
Article
Full-text available
Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.
... Willingness to share AV rides was found to be positively correlated with familiarity of the AV and ride-sharing technology possibly attenuating the risk-perception for this future transport mode. The aversion of sharing rides was reported to remain at a rather high level during the pandemic in the US according to Jabbari and MacKenzie (2020). Respondents in this questionnaire survey reported feeling uncomfortable with sharing a ride with strangers both before and during the pandemic, while in the latter case people appeared more reluctant to share rides to save money or even trying more to use transportation modes that avoid contact with other people compared to their responses before the pandemic. ...
Article
Shared electric automated vehicles (AVs) are advertised as the silver bullet for the sustainable transition of private internal combustion engine-based automobility by private and public entities. We explore the extent to which private automobility will be reconfigured into a private electric automated automobility regime or substituted by a shared electric automated automobility regime that could effectively address societal sustainability challenges. We draw from the multi-level perspective of technological transition, develop a conceptual model outlining possible transition advancements towards private and shared electric automated automobility and review pertinent literature supporting such developments. Our analysis reveals that shared, particularly pooled, mobility emerges slowly (niche level). Key actors resist a shift from private to shared electric automated mobility for economic (vehicle manufacturers), instrumental, affective, symbolic (users and societal groups), tax-revenue, governance and administrative (public authorities) reasons (regime level). The private automobility regime receives only moderate pressure from the socio-technical landscape pertaining to safety, congestion and environmental issues and effectively reacts by electrifying and automating vehicles (landscape level). We conclude that the most likely transition will primarily entail privately-owned electric AVs as opposed to shared (especially pooled) AVs. Hence, the socioeconomic benefits of the so-called "three revolutions of automobility" could be diminished.
... In the U.S., the tradeoff between sharing rides and the cost of transportation changed, during the pandemic. Compared to the pre-pandemic period, individuals were less willing to share rides with strangers (Jabbari & MacKenzie, 2020). In contrast to ridesourcing demand, during the pandemic, bike share demand was more resilient in Chicago, New York and London though it contributed to increasing COVID-19 cases in Chicago Li et al., 2021;Padmanabhan et al., 2021;Teixeira & Lopes, 2020). ...
Article
Sumario: 1. Introducción. Movilidad urbana, regulación y competencia.—2. El impacto de la digitalización y las nuevas formas de movilidad urbana.—3. Pronunciamientos de la CNMC sobre movilidad urbana.—4. Tendencias regulatorias ante las nuevas formas de movilidad urbana.—5. Conclusiones. Principios para una buena regulación de la movilidad.—Bibliografía.
Book
Incorporating a hands-on pedagogical approach, Nonparametric Statistics for Social and Behavioral Sciences presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM’s SPSS software. This text is the only current nonparametric book written specifically for students in the behavioral and social sciences. Emphasizing sound research designs, appropriate statistical analyses, and accurate interpretations of results, the text: • Explains a conceptual framework for each statistical procedure • Presents examples of relevant research problems, associated research questions, and hypotheses that precede each procedure • Details SPSS paths for conducting various analyses • Discusses the interpretations of statistical results and conclusions of the research With minimal coverage of formulas, the book takes a nonmathematical approach to nonparametric data analysis procedures and shows students how they are used in research contexts. Each chapter includes examples, exercises, and SPSS screen shots illustrating steps of the statistical procedures and resulting output.
Median Age Doesn’t Tell the Whole Story, The United States Census Bureau
  • U.S.Census Bureau
Income and Poverty in the United States: 2019
  • US Census Bureau
Median Age Doesn’t Tell the Whole Story
  • US Census Bureau